


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCamelCase__							:						Optional[Any]							     =   logging.get_logger(__name__)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       ['input_features', 'is_longer']

     def __init__(					self:List[Any]     ,		_a:Dict=64     ,		_a:List[str]=4_80_00     ,		_a:str=4_80     ,		_a:Tuple=10     ,		_a:Dict=10_24     ,		_a:Any=0.0     ,		_a:List[Any]=False     ,		_a:float = 0     ,		_a:float = 1_40_00     ,		_a:int = None     ,		_a:str = "fusion"     ,		_a:str = "repeatpad"     ,		**_a:str     ,		):
       super().__init__(
           feature_size=_a     ,		sampling_rate=_a     ,		padding_value=_a     ,		return_attention_mask=_a     ,		**_a     ,		)
       snake_case__						= top_db
       snake_case__						= truncation
       snake_case__						= padding
       snake_case__						= fft_window_size
       snake_case__						= (fft_window_size >> 1) + 1
       snake_case__						= hop_length
       snake_case__						= max_length_s
       snake_case__						= max_length_s * sampling_rate
       snake_case__						= sampling_rate
       snake_case__						= frequency_min
       snake_case__						= frequency_max
       snake_case__						= mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=_a     ,		min_frequency=_a     ,		max_frequency=_a     ,		sampling_rate=_a     ,		norm=_a     ,		mel_scale='''htk'''     ,		)
       snake_case__						= mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=_a     ,		min_frequency=_a     ,		max_frequency=_a     ,		sampling_rate=_a     ,		norm='''slaney'''     ,		mel_scale='''slaney'''     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= copy.deepcopy(self.__dict__   )
       snake_case__						= self.__class__.__name__
       if "mel_filters" in output:
         del output["mel_filters"]
       if "mel_filters_slaney" in output:
         del output["mel_filters_slaney"]
       return output

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:np.array     ,		_a:Optional[np.array] = None   ):
       snake_case__						= spectrogram(
           _a     ,		window_function(self.fft_window_size     ,		'''hann'''   )     ,		frame_length=self.fft_window_size     ,		hop_length=self.hop_length     ,		power=2.0     ,		mel_filters=_a     ,		log_mel='''dB'''     ,		)
       return log_mel_spectrogram.T

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:List[str]     ,		_a:Dict     ,		_a:int   ):
       snake_case__						= np.array_split(list(range(0     ,		total_frames - chunk_frames + 1   )   )     ,		3   )
       if len(ranges[1]   ) == 0:
         # if the audio is too short, we just use the first chunk
         snake_case__						= [0]
       if len(ranges[2]   ) == 0:
         # if the audio is too short, we just use the first chunk
         snake_case__						= [0]
       # randomly choose index for each part
       snake_case__						= np.random.choice(ranges[0]   )
       snake_case__						= np.random.choice(ranges[1]   )
       snake_case__						= np.random.choice(ranges[2]   )

       snake_case__						= mel[idx_front : idx_front + chunk_frames, :]
       snake_case__						= mel[idx_middle : idx_middle + chunk_frames, :]
       snake_case__						= mel[idx_back : idx_back + chunk_frames, :]

       snake_case__						= torch.tensor(mel[None, None, :]   )
       snake_case__						= torch.nn.functional.interpolate(
           _a     ,		size=[chunk_frames, 64]     ,		mode='''bilinear'''     ,		align_corners=_a   )
       snake_case__						= mel_shrink[0][0].numpy()
       snake_case__						= np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,		axis=0   )
       return mel_fusion

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:np.array     ,		_a:Dict     ,		_a:List[str]     ,		_a:Union[str, Any]   ):
       if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
           snake_case__						= True
           # random crop to max_length (for compatibility) -> this should be handled by self.pad
           snake_case__						= len(_a   ) - max_length
           snake_case__						= np.random.randint(0     ,		overflow + 1   )
           snake_case__						= waveform[idx : idx + max_length]
           snake_case__						= self._np_extract_fbank_features(_a     ,		self.mel_filters_slaney   )[None, :]
         elif truncation == "fusion":
           snake_case__						= self._np_extract_fbank_features(_a     ,		self.mel_filters   )
           snake_case__						= max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
           snake_case__						= mel.shape[0]
           if chunk_frames == total_frames:
             # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
             # In this case, we just use the whole audio.
             snake_case__						= np.stack([mel, mel, mel, mel]     ,		axis=0   )
             snake_case__						= False
           else:
             snake_case__						= self._random_mel_fusion(_a     ,		_a     ,		_a   )
             snake_case__						= True
         else:
           raise NotImplementedError(F"""data_truncating {truncation} not implemented"""   )

       else:
         snake_case__						= False
         # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
         if waveform.shape[0] < max_length:
           if padding == "repeat":
             snake_case__						= int(max_length / len(_a   )   )
             snake_case__						= np.stack(np.tile(_a     ,		n_repeat + 1   )   )[:max_length]
           if padding == "repeatpad":
             snake_case__						= int(max_length / len(_a   )   )
             snake_case__						= np.stack(np.tile(_a     ,		_a   )   )
           snake_case__						= np.pad(_a     ,		(0, max_length - waveform.shape[0])     ,		mode='''constant'''     ,		constant_values=0   )

         if truncation == "fusion":
           snake_case__						= self._np_extract_fbank_features(_a     ,		self.mel_filters   )
           snake_case__						= np.stack([input_mel, input_mel, input_mel, input_mel]     ,		axis=0   )
         else:
           snake_case__						= self._np_extract_fbank_features(_a     ,		self.mel_filters_slaney   )[None, :]

       return input_mel, longer







     def __call__(					self:Union[str, Any]     ,		_a:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		_a:str = None     ,		_a:Optional[str] = None     ,		_a:Optional[int] = None     ,		_a:Optional[int] = None     ,		_a:Optional[Union[str, TensorType]] = None     ,		**_a:int     ,		):
       snake_case__						= truncation if truncation is not None else self.truncation
       snake_case__						= padding if padding else self.padding

       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
               F""" was sampled with {self.sampling_rate} and not {sampling_rate}."""   )
       else:
         logger.warning(
             '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
             '''Failing to do so can result in silent errors that might be hard to debug.'''   )

       snake_case__						= isinstance(_a     ,		np.ndarray   ) and len(raw_speech.shape   ) > 1
       if is_batched_numpy and len(raw_speech.shape   ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
       snake_case__						= is_batched_numpy or (
           isinstance(_a     ,		(list, tuple)   ) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)   ))
       )

       if is_batched:
         snake_case__						= [np.asarray(_a     ,		dtype=np.floataa   ) for speech in raw_speech]
       elif not is_batched and not isinstance(_a     ,		np.ndarray   ):
         snake_case__						= np.asarray(_a     ,		dtype=np.floataa   )
       elif isinstance(_a     ,		np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
         snake_case__						= raw_speech.astype(np.floataa   )

       # always return batch
       if not is_batched:
         snake_case__						= [np.asarray(_a   )]

       # convert to mel spectrogram, truncate and pad if needed.
       snake_case__						= [
           self._get_input_mel(_a     ,		max_length if max_length else self.nb_max_samples     ,		_a     ,		_a   )
           for waveform in raw_speech
       ]

       snake_case__						= []
       snake_case__						= []
       for mel, longer in padded_inputs:
         input_mel.append(_a   )
         is_longer.append(_a   )

       if truncation == "fusion" and sum(_a   ) == 0:
         # if no audio is longer than 10s, then randomly select one audio to be longer
         snake_case__						= np.random.randint(0     ,		len(_a   )   )
         snake_case__						= True

       if isinstance(input_mel[0]     ,		_a   ):
         snake_case__						= [np.asarray(_a     ,		dtype=np.floataa   ) for feature in input_mel]

       # is_longer is a list of bool
       snake_case__						= [[longer] for longer in is_longer]

       snake_case__						= {'''input_features''': input_mel, '''is_longer''': is_longer}
       snake_case__						= BatchFeature(_a   )

       if return_tensors is not None:
         snake_case__						= input_features.convert_to_tensors(_a   )

       return input_features


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')